Advanced Machine Learning with Evolutionary and Metaheuristic Techniques PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Advanced Machine Learning with Evolutionary and Metaheuristic Techniques PDF full book. Access full book title Advanced Machine Learning with Evolutionary and Metaheuristic Techniques by Jayaraman Valadi. Download full books in PDF and EPUB format.
Author: Jayaraman Valadi
Publisher: Springer Nature
ISBN: 9819997186
Category :
Languages : en
Pages : 365
Get Book Here
Book Description
Author: Jayaraman Valadi
Publisher: Springer Nature
ISBN: 9819997186
Category :
Languages : en
Pages : 365
Get Book Here
Book Description
Author: Diego Oliva
Publisher: Springer Nature
ISBN: 3030705420
Category : Technology & Engineering
Languages : en
Pages : 766
Get Book Here
Book Description
This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.
Author: Ivan Zelinka
Publisher: Springer
ISBN: 3642107079
Category : Technology & Engineering
Languages : en
Pages : 533
Get Book Here
Book Description
This book discusses the mutual intersection of two fields of research: evolutionary computation, which can handle tasks such as control of various chaotic systems, and deterministic chaos, which is investigated as a behavioral part of evolutionary algorithms.
Author: R., Thanigaivelan
Publisher: IGI Global
ISBN:
Category : Computers
Languages : en
Pages : 423
Get Book Here
Book Description
In contemporary engineering domains, optimization and decision-making issues are crucial. Given the vast amounts of available data, processing times and memory usage can be substantial. Developing and implementing novel heuristic algorithms is time-consuming, yet even minor improvements in solutions can significantly reduce computational costs. In such scenarios, the creation of heuristics and metaheuristic algorithms has proven advantageous. The convergence of machine learning and metaheuristic algorithms offers a promising approach to address these challenges. Metaheuristic and Machine Learning Optimization Strategies for Complex Systems covers all areas of comprehensive information about hyper-heuristic models, hybrid meta-heuristic models, nature-inspired computing models, and meta-heuristic models. The key contribution of this book is the construction of a hyper-heuristic approach for any general problem domain from a meta-heuristic algorithm. Covering topics such as cloud computing, internet of things, and performance evaluation, this book is an essential resource for researchers, postgraduate students, educators, data scientists, machine learning engineers, software developers and engineers, policy makers, and more.
Author: Diego Oliva
Publisher: Springer Nature
ISBN: 3030409775
Category : Technology & Engineering
Languages : en
Pages : 488
Get Book Here
Book Description
This book presents a collection of the most recent hybrid methods for image processing. The algorithms included consider evolutionary, swarm, machine learning and deep learning. The respective chapters explore different areas of image processing, from image segmentation to the recognition of objects using complex approaches and medical applications. The book also discusses the theory of the methodologies used to provide an overview of the applications of these tools in image processing. The book is primarily intended for undergraduate and postgraduate students of science, engineering and computational mathematics, and can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence. Further, it is a valuable resource for researchers from the evolutionary computation, artificial intelligence and image processing communities.
Author: Erik Cuevas
Publisher: John Wiley & Sons
ISBN: 139422964X
Category : Computers
Languages : en
Pages : 437
Get Book Here
Book Description
Learn to bridge the gap between machine learning and metaheuristic methods to solve problems in optimization approaches Few areas of technology have greater potential to revolutionize the globe than artificial intelligence. Two key areas of artificial intelligence, machine learning and metaheuristic computation, have an enormous range of individual and combined applications in computer science and technology. To date, these two complementary paradigms have not always been treated together, despite the potential of a combined approach which maximizes the utility and minimizes the drawbacks of both. Machine Learning and Metaheuristic Computation offers an introduction to both of these approaches and their joint applications. Both a reference text and a course, it is built around the popular Python programming language to maximize utility. It guides the reader gradually from an initial understanding of these crucial methods to an advanced understanding of cutting-edge artificial intelligence tools. The text also provides: Treatment suitable for readers with only basic mathematical training Detailed discussion of topics including dimensionality reduction, clustering methods, differential evolution, and more A rigorous but accessible vision of machine learning algorithms and the most popular approaches of metaheuristic optimization Machine Learning and Metaheuristic Computation is ideal for students, researchers, and professionals looking to combine these vital methods to solve problems in optimization approaches.
Author: Francisco Almeida
Publisher: Springer Science & Business Media
ISBN: 3540463844
Category : Computers
Languages : en
Pages : 202
Get Book Here
Book Description
This book constitutes the refereed proceedings of the Third International Workshop on Hybrid Metaheuristics, HM 2006, held in Gran Canaria, Spain, in October 2006. The 13 revised full papers presented together with one invited paper were carefully reviewed and selected from 42 submissions.
Author: Claude Sammut
Publisher: Springer Science & Business Media
ISBN: 0387307680
Category : Computers
Languages : en
Pages : 1061
Get Book Here
Book Description
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
Author: H S Madhusudhan
Publisher: CRC Press
ISBN: 1040112641
Category : Computers
Languages : en
Pages : 263
Get Book Here
Book Description
This book focuses on the current trends in research and analysis of virtual machine placement in a cloud data center. It discusses the integration of machine learning models and metaheuristic approaches for placement techniques. Taking into consideration the challenges of energy-efficient resource management in cloud data centers, it emphasizes upon computing resources being suitably utilised to serve application workloads in order to reduce energy utilisation, while maintaining apt performance. This book provides information on fault-tolerant mechanisms in the cloud and provides an outlook on task scheduling techniques. Focuses on virtual machine placement and migration techniques for cloud data centers Presents the role of machine learning and metaheuristic approaches for optimisation in cloud computing services Includes application of placement techniques for quality of service, performance, and reliability improvement Explores data center resource management, load balancing and orchestration using machine learning techniques Analyses dynamic and scalable resource scheduling with a focus on resource management The text is for postgraduate students, professionals, and academic researchers working in the fields of computer science and information technology.
Author: Mansour Eddaly
Publisher: Springer Nature
ISBN: 9811938881
Category : Computers
Languages : en
Pages : 231
Get Book Here
Book Description
Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.
